To summarise, in this work we will compare three architectures: two architectures presented in our earlier works, i.e., CNN U-Net framework [1]}, and MAgNET framework [2]}, as well as the attention-based architecture, Perceiver IO [3]}, which hasn't been explored for its applications in mechanics yet. We show the capabilities of three frameworks by learning on non-linear FEM datasets and by cross-comparing their performance. In Section , we will introduce the three DNN architectures, in Section , we will provide the study of their performance, and in Section  we will summarize the results and discuss future directions.
